Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Eval Results
Instructions to use google/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use google/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("google/embeddinggemma-300m") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Multimodal Embedding Gemma Incoming?
#37
by LorryG - opened
I see that T5 Gemma 2 is multimodal, using a SigLip image encoder. How likely are we to get a multimodal early-fusion embedding model?
Hi @LorryG
While we can't comment on the likelihood of specific future releases, your feedback regarding early-fusion models has been forwarded to our developers.Thanks